Communication towers are vital assets in our daily lives as they transfer signals between cell phones facilitating communication and commerce among people and businesses all around the world. Wind loads are crucial in the communication towers design since they are tall and slender. With climate change bringing more storms and higher wind speeds, it is more crucial to research the finest tower structure that withstands such conditions with the least life cycle cost. Therefore, in this paper, a comparative case study is performed between 45 m height lattice tower and monopole tower in Egypt. Two

Telecommunication towers are essential infrastructure in today's fast-paced world. Lattice self-supporting towers, monopole towers, and guyed towers are the three types of structures that can be used for telecommunications towers. When analyzing telecom tower loads, wind loads are the most important ones to address. As a result, it is necessary to choose an appropriate structure that can withstand the wind and the surrounding environment. The main aim of this paper is to propose a guideline for selecting the optimum tower structure based on the surrounding environment. In order to create this

Today, automatic food/non-food classification became extremely important for many real-time applications, specifically since the pandemic of the COVID-19 virus. Such that the 'no food policy' now became applied more than ever to help decrease the spread of the COVID-19 virus. Consequently, many studies used deep neural networks for the food/non-food classification task, yet these deep neural networks were computationally expensive. As a result, in this paper, a lightweight Convolution Neural Network (CNN) is proposed and put into use for classifying foods and non-foods. Compared to prior

Due to the advances in machine learning techniques, recommender systems nowadays are capable of learning and influencing the users' decisions. Hence, recommendations became an important facility to reduce the cost (or increase the profit) of the operators of the demand networks. In this paper we formulate and study the problem of dynamically optimizing the demand shaping, through content recommendation, and proactive caching. The formulated problem suffers from the curse of dimensionality, so we devise an approximate algorithm optimizing only over a short look-ahead window. The approximate

As the main pillar of the Smart City, Smart Highway manifests the centralized connectivity concept between the self-driving vehicles. Internet of Vehicle or IoV is the solution for improved connectivity between driverless vehicles. One of the major challenges in IoV research is the lack of datasets available. That is why the Internet of vehicles is one of the hot topics in research nowadays. IoV field is still a new topic in research, which leaves a huge shortage in the datasets available to train any Artificial Intelligent (AI) model for IoV systems. IoV systems have many research points such

Smart cars, with the emergence of the Internet of Vehicles (IoV), are expected to generate huge volumes of data at rates that typical data management systems will not be able to handle. Such data can be extremely useful to both analytics and machine learning applications. This paper discusses and demonstrates the process of architecting and building a scalable data management system for the IoV in a smart city environment, using Apache Spark, Apache Kafka and Apache Cassandra, which results in a scalable, resilient and fault-Tolerant data management system that facilitates performing big data

In this paper we study the problem of optimal power control for a Z-interference channel abstracting an underlay cognitive radio network where the secondary user has delay constraints. More specifically, we minimize the packet drop probability at the secondary user (equivalent to delay bound violation probability) subject to quality of service (QoS) constraints at the primary and secondary users, among other constraints. Towards, this objective, we develop a mathematical framework using Markov chains and formulate a constrained optimization problem. First, we assess the complexity of the

The massive data exchange between base stations and network backhaul creates a strong overhead on mobile networks, especially at peak times. This motivates researchers to think about the proactive caching concept which depends mainly on caching some of the expected data items during off-peak times. The caching problem consists of two distinct phases, placement phase and delivery phase. In this work, we consider a mobility-aware device-to-device (D2D) caching network. We assume that data contents were already cached in users devices during the content placement phase and we focus on the content

Space frequency block coding with orthogonal frequency division multiplexing (SFBC-OFDM) suffers from the effect of inter-carrier interference (ICI) in doubly-selective communication channels. In this paper, a scheme is proposed in which windowing is applied to the received signal to reduce the effect of ICI to a limited number of neighboring sub-carriers. The sub-carriers holding the SFBC components of each codeword are separated by a number of sub-carriers larger than the ICI range, and hence, they do not interfere with each other. In order to preserve the structure of the SFBC, the

In this paper, we present two practical ARQ-Based security schemes for Wi-Fi and RFID networks. Our proposed schemes enhance the confidentiality and authenticity functions of these networks, respectively. Both schemes build on the same idea; by exploiting the statistical independence between the multipath fading experienced by the legitimate nodes and potential adversaries, secret keys are established and then are continuously updated. The continuous key update property of both schemes makes them capable of defending against all of the passive eavesdropping attacks and most of the currently